Modern day medical and biomolecular imaging scanners can generate large amounts of data in a short period of time, usually requiring a dedicated computer for processing and visualization. For indirect medical imaging modalities/devices, such as MRI, PET and CT, the raw data, commonly called k-space data, needs to be mathematically transformed into medical images which require super scale computing power. This process, called medical image reconstruction, can take hours using current systems and severely limits clinical use of medial imaging applications.
Recent advances in multi-core computer processor technology will drastically reduce medical image processing time. It will also open the door to new possibilities of sharing computer intensive processors among the modalities. Emerging multi-core processors are able to accelerate medical imaging applications by exploiting the parallelism available in their algorithms. Unfortunately all existing systems require a separate processing system for each imaging device, which is both costly and decentralized. Moreover, modern day medical and biomolecular imaging scanners can generate huge amounts of data in a short period of time, usually requiring a dedicated computer for processing and visualization. In view of the foregoing, there exists a need for